I am a fourth-year Ph.D. student in Quebec Artificial Intelligence Institute (AKA Mila) and Université de Montréal, advised by Prof. Jian Tang.
Before joining Mila, I got the CS master’s degree from University of Wisconsin-Madison, and was the graduate researcher at Morgridge Institute for Research. During my stay in UW-Madison, I started my first research project and was fortunately advised by Prof. Anthony Gitter, Prof. Yingyu Liang, and Prof. Dimitris Papailiopoulos. Proir to that, I got my bachelor’s degree from Shandong University.
I also want to share some inspiring research values (special thanks to Weiyang):
- Focus on creating novel ideas, not publishing papers
- Follow curiosity and passion, not trends
- Ideas are not owned, but come with debts to those who came before
- Ideas become stronger when shared, discussed and criticized
- Life is surprisingly short, so solve problems that interest and excite you most
- It is good to be quick, but it is more important to be deep
- Think like an amateur, do as an expert
- Last lecture by Prof. Randy Pausch
- [My own words] We are only responsbile for doing fancy research projects and writing fancy papers; for the rest, we can leave them to fate.
Research Topics
- Representation Learning and Distribution Modeling
- structured/graph/geometric representation learning
- self-supervised learning
- multi-task learning
- deep generative modeling
- controllable deep generative modeling (editing)
- Drug Discovery
- small molecule
- protein
- Learning Dynamics
Selected Publications
Artificial Intelligence Generated Drug (AIGD)
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ProteinDT: A Text-guided Protein Design Framework
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar
[Project Page] [ArXiv] [Code] -
MoleculeSTM: Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval
Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Anima Anandkumar
[Project Page] [ArXiv] [Code]
[NeurIPS AI4Science Workshop 2022] -
GraphCG: Unsupervised Discovery of Steerable Factors in Graphs
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou, Jian Tang
[Project Page] [ArXiv] [Code]
[NeurIPS GLFrontiers Workshop 2022 Oral]
Transfer Learning
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GeoSSL: Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
Shengchao Liu, Hongyu Guo, Jian Tang
ICLR 2023
[Project Page] [Paper] [ArXiv] [Code] -
GraphMVP: Pre-training Molecular Graph Representation with 3D Geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
ICLR 2022
[Project Page] [Paper] [ArXiv] [Code] [Slides] [Poster]
[NeurIPS SSL Workshop 2021]
[ICLR GTRL Workshop 2022 Spotlight] -
SGNN-EBM: Structured Multi-task Learning for Molecular Property Prediction
Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang
AISTATS 2022
[Project Page] [Paper] [ArXiv] [Code] [Slides] [Poster]
[NeurIPS AI4Science Workshop 2021] -
LBTW: Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
Shengchao Liu, Yingyu Liang, Anthony Gitter
AAAI-Student Abstract 2019
[Paper] [Appendix] [Code] [Poster]
Representation Learning
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AWARE: Attentive Walk-Aggregating Graph Neural Networks
Mehmet F. Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, Yingyu Liang
TMLR 2022
[Paper] [ArXiv] [Code] -
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang
NeurIPS 2019 Spotlight
[Paper] [ArXiv] [Code] [Slides][Poster]
[NeurIPS MLMM Workshop 2018]
Learning Dynamics
- Bad Global Minima Exist and SGD Can Reach Them
Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
NeurIPS 2020
[Paper] [Code] [Slides] [Poster] [Video/Audio, NeurIPS 2020]
[ICML Deep Learning Phenomena Workshop 2019 Oral]
Interests
- Coding. Like ACM-ICPC and Capture the Flag.
- Video Editing. Quite cool stuff.
- Reading. Novels are my favourite, but also history and computer books.
- Music. Classical music are perfect match for coding.